Abstract
Scientific applications are becoming data intensive, and traditional load-balance solutions require reconsideration for scaling data and computation in various parallel systems. This chapter examines state-transition applications, which is a representative scientific application that handles grand-challenging problems (e.g., weather forecasting and ocean prediction) and relates to intensive data. We propose an adaptive workload partitioning and allocation scheme for parallelizing state-transition applications in various parallel systems. Existing schemes insufficiently balance both computation of complicated scientific algorithms and increasing volumes of scientific data simultaneously. Our solution addresses this problem by introducing a time metric to unify the workloads of computation and data. System profiles in terms of CPU and I/O speeds are considered for embracing system diversity, suggesting accurate estimation of workload. The solution consists of two major components: (1) an adaptive decomposition scheme that uses the quad-tree structure to break up workload and manage data dependency; and (2) a decentralized scheme for distributing workload across processors. Experimental results from real-world weather data demonstrate that the solution outperforms other partitioning schemes, and can be readily ported to diverse systems with satisfactory performance.
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Yang, X., Li, X. (2014). Adaptive Workload Partitioning and Allocation for Data Intensive Scientific Applications. In: Li, X., Qiu, J. (eds) Cloud Computing for Data-Intensive Applications. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-1905-5_6
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DOI: https://doi.org/10.1007/978-1-4939-1905-5_6
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